mani-skill/ManiSkill

Health v2 · maintenance only

Adoption not part of the score

3264 stars · 535 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ManiSkill is an open-source GPU-parallelized robotics simulation framework and benchmark built on SAPIEN, focused on manipulation skills. It provides high-throughput synthetic data collection, a task-building API, and tuned baselines for reinforcement learning, imitation learning, and vision-language-action models.

Use cases

• train robot manipulation policies with reinforcement learning
• collect large-scale synthetic RGBD and segmentation data on GPU
• benchmark robot learning algorithms across manipulation tasks
• run sim2real transfer of policies to real robots
• build custom simulation tasks for heterogeneous parallel environments
• evaluate vision-language-action models like Octo or RDT
• simulate humanoids and mobile manipulators in parallel

When to choose

• you need GPU-parallelized simulation for high-throughput robot learning
• you work on manipulation skills, dexterous hands, or mobile manipulation
• you want ready-made RL/IL/VLA baselines and benchmarks
• you need real2sim evaluation or sim2real deployment examples

When to avoid

• you only need lightweight physics simulation without GPU parallelism
• your focus is locomotion or navigation rather than manipulation
• you need a non-Python workflow or non-GPU hardware

Facets

framework · maturity active

simulation reinforcement-learning machine-learning benchmarking robotics robotics reinforcement-learning simulation artificial-intelligence machine-learning python cross-platform robot-manipulation embodied-ai sim2real gpu-parallel-simulation sapien robot-learning benchmark gpu linux

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markdown · JSON · MCP: product_card(name="mani-skill/ManiSkill")